Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "125" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 29 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460012 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.337961 | 0.014618 | 0.953847 | 1.014647 | -0.075277 | 0.075943 | 0.129642 | 0.214068 | 0.6052 | 0.6110 | 0.3435 | nan | nan |
| 2460011 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.381141 | -0.187759 | 0.856269 | 1.341256 | 3.471997 | 0.473313 | 2.058442 | 1.191598 | 0.6202 | 0.6316 | 0.3384 | nan | nan |
| 2460010 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.955577 | 0.731029 | 0.735714 | 1.251840 | 0.684258 | 0.149276 | 1.923459 | 0.617476 | 0.6318 | 0.6481 | 0.3398 | nan | nan |
| 2460009 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.210513 | 2.752004 | 1.442210 | 1.605884 | 0.817161 | 0.202629 | 0.953964 | 0.951305 | 0.6243 | 0.6415 | 0.3493 | nan | nan |
| 2460008 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.292313 | 0.461314 | 1.234248 | 1.683739 | 0.055395 | -0.075471 | 0.430446 | 1.427494 | 0.6764 | 0.6883 | 0.3028 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.090368 | 0.688874 | 1.052913 | 1.340877 | 0.755896 | 0.316134 | 6.448698 | 1.836113 | 0.6356 | 0.6514 | 0.3250 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 89.06% | 85.63% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1301 | 0.1512 | 0.0532 | nan | nan |
| 2459998 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.428302 | -0.090928 | 0.710933 | 0.997754 | 1.936396 | 1.237239 | 3.796980 | 3.964216 | 0.6282 | 0.6372 | 0.3675 | nan | nan |
| 2459997 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.069677 | -0.099275 | 0.778039 | 1.243455 | 1.601942 | -0.518992 | 1.237837 | 0.367110 | 0.6377 | 0.6495 | 0.3734 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 99.08% | 99.03% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.5319 | 0.5134 | 0.3408 | nan | nan |
| 2459995 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.453750 | 0.237981 | 0.343991 | 1.294419 | 0.341925 | 1.482356 | 0.932277 | 3.419676 | 0.6377 | 0.6434 | 0.3658 | nan | nan |
| 2459994 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.484813 | 1.795845 | 0.209450 | 1.125424 | 0.753989 | 2.933354 | 1.262238 | 2.057053 | 0.6338 | 0.6393 | 0.3630 | nan | nan |
| 2459993 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.254056 | -0.132989 | -0.161369 | 0.967045 | 1.134100 | 0.809066 | 1.653107 | 2.070223 | 0.6261 | 0.6452 | 0.3724 | nan | nan |
| 2459991 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.059819 | 0.052217 | -0.073295 | 1.002195 | 1.163240 | 0.310283 | 1.090616 | 0.933235 | 0.6441 | 0.6489 | 0.3723 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.606628 | 1.722522 | -0.100592 | 0.805901 | 1.031838 | 1.712630 | 0.665013 | 3.012761 | 0.6413 | 0.6444 | 0.3660 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.464635 | 2.074645 | -0.088102 | 0.950814 | 1.654227 | 0.249397 | 1.676043 | 1.537523 | 0.6320 | 0.6418 | 0.3663 | nan | nan |
| 2459988 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.151926 | 0.223380 | -0.101167 | 0.759977 | 1.520453 | 0.843076 | 0.583484 | 1.210222 | 0.6373 | 0.6454 | 0.3611 | nan | nan |
| 2459987 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.262661 | -0.944873 | 0.166147 | 1.096857 | -0.469401 | -0.790833 | -0.540998 | -0.445336 | 0.6438 | 0.6509 | 0.3628 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.438166 | -0.659804 | 0.146252 | 1.031037 | -0.401283 | -0.690331 | -0.048791 | -0.189143 | 0.6673 | 0.6778 | 0.3187 | nan | nan |
| 2459985 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.190525 | -0.850520 | 0.229440 | 1.126177 | -0.330061 | -0.907748 | 0.461614 | -0.310161 | 0.6447 | 0.6521 | 0.3725 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.056470 | -0.288907 | 0.450817 | 1.302518 | -0.349712 | -0.547824 | -0.530438 | -0.348994 | 0.6618 | 0.6683 | 0.3500 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.354216 | 0.671378 | -0.103895 | 0.868174 | 0.676824 | 1.600921 | -0.018361 | 1.617989 | 0.6750 | 0.6883 | 0.3050 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.058625 | 0.731555 | -0.000535 | 0.892161 | -0.281707 | -0.177854 | 0.023877 | 0.589367 | 0.7279 | 0.7272 | 0.2656 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.903722 | 1.291448 | -0.369093 | 0.741510 | 1.375937 | 0.768986 | 1.852643 | 1.057534 | 0.6444 | 0.6534 | 0.3601 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.302052 | 0.820838 | -0.250230 | 0.827017 | 0.895558 | 1.580724 | -0.034585 | 1.046658 | 0.6880 | 0.6945 | 0.2834 | nan | nan |
| 2459979 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.614954 | 0.034477 | -0.465654 | 0.669638 | 0.019333 | -0.014313 | 0.130716 | 0.090589 | 0.6383 | 0.6500 | 0.3656 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.863563 | 2.034738 | -0.487042 | 0.669683 | 0.098743 | -0.338151 | 0.380836 | 0.026126 | 0.6370 | 0.6476 | 0.3693 | nan | nan |
| 2459977 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.592630 | 0.374039 | -0.370494 | 0.794743 | 0.003397 | -0.350757 | 0.256618 | -0.192807 | 0.6073 | 0.6160 | 0.3385 | nan | nan |
| 2459976 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.902612 | -0.934723 | -0.352237 | 0.786665 | 0.700316 | -0.509494 | 0.524209 | -0.253232 | 0.6486 | 0.6561 | 0.3609 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.014647 | -0.337961 | 0.014618 | 0.953847 | 1.014647 | -0.075277 | 0.075943 | 0.129642 | 0.214068 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Variability | 3.471997 | 1.381141 | -0.187759 | 0.856269 | 1.341256 | 3.471997 | 0.473313 | 2.058442 | 1.191598 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Discontinuties | 1.923459 | 0.955577 | 0.731029 | 0.735714 | 1.251840 | 0.684258 | 0.149276 | 1.923459 | 0.617476 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Shape | 2.752004 | 2.210513 | 2.752004 | 1.442210 | 1.605884 | 0.817161 | 0.202629 | 0.953964 | 0.951305 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.683739 | 0.461314 | 0.292313 | 1.683739 | 1.234248 | -0.075471 | 0.055395 | 1.427494 | 0.430446 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Discontinuties | 6.448698 | 3.090368 | 0.688874 | 1.052913 | 1.340877 | 0.755896 | 0.316134 | 6.448698 | 1.836113 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Discontinuties | 3.964216 | 0.428302 | -0.090928 | 0.710933 | 0.997754 | 1.936396 | 1.237239 | 3.796980 | 3.964216 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Variability | 1.601942 | 0.069677 | -0.099275 | 0.778039 | 1.243455 | 1.601942 | -0.518992 | 1.237837 | 0.367110 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Discontinuties | 3.419676 | 0.453750 | 0.237981 | 0.343991 | 1.294419 | 0.341925 | 1.482356 | 0.932277 | 3.419676 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Variability | 2.933354 | 0.484813 | 1.795845 | 0.209450 | 1.125424 | 0.753989 | 2.933354 | 1.262238 | 2.057053 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Discontinuties | 2.070223 | 0.254056 | -0.132989 | -0.161369 | 0.967045 | 1.134100 | 0.809066 | 1.653107 | 2.070223 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Variability | 1.163240 | 1.059819 | 0.052217 | -0.073295 | 1.002195 | 1.163240 | 0.310283 | 1.090616 | 0.933235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Discontinuties | 3.012761 | 1.722522 | 0.606628 | 0.805901 | -0.100592 | 1.712630 | 1.031838 | 3.012761 | 0.665013 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 5.464635 | 2.074645 | 5.464635 | 0.950814 | -0.088102 | 0.249397 | 1.654227 | 1.537523 | 1.676043 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Temporal Variability | 1.520453 | 0.223380 | 1.151926 | 0.759977 | -0.101167 | 0.843076 | 1.520453 | 1.210222 | 0.583484 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.096857 | 0.262661 | -0.944873 | 0.166147 | 1.096857 | -0.469401 | -0.790833 | -0.540998 | -0.445336 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.031037 | -0.659804 | 0.438166 | 1.031037 | 0.146252 | -0.690331 | -0.401283 | -0.189143 | -0.048791 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.126177 | -0.850520 | 0.190525 | 1.126177 | 0.229440 | -0.907748 | -0.330061 | -0.310161 | 0.461614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 1.302518 | -0.056470 | -0.288907 | 0.450817 | 1.302518 | -0.349712 | -0.547824 | -0.530438 | -0.348994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Temporal Discontinuties | 1.617989 | 0.354216 | 0.671378 | -0.103895 | 0.868174 | 0.676824 | 1.600921 | -0.018361 | 1.617989 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 2.058625 | 2.058625 | 0.731555 | -0.000535 | 0.892161 | -0.281707 | -0.177854 | 0.023877 | 0.589367 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 3.903722 | 1.291448 | 3.903722 | 0.741510 | -0.369093 | 0.768986 | 1.375937 | 1.057534 | 1.852643 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 4.302052 | 0.820838 | 4.302052 | 0.827017 | -0.250230 | 1.580724 | 0.895558 | 1.046658 | -0.034585 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 3.614954 | 3.614954 | 0.034477 | -0.465654 | 0.669638 | 0.019333 | -0.014313 | 0.130716 | 0.090589 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 4.863563 | 2.034738 | 4.863563 | 0.669683 | -0.487042 | -0.338151 | 0.098743 | 0.026126 | 0.380836 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | nn Power | 0.794743 | 0.592630 | 0.374039 | -0.370494 | 0.794743 | 0.003397 | -0.350757 | 0.256618 | -0.192807 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 125 | N09 | RF_maintenance | ee Shape | 1.902612 | -0.934723 | 1.902612 | 0.786665 | -0.352237 | -0.509494 | 0.700316 | -0.253232 | 0.524209 |